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Enabling Smart Supply Chain Management with Artificial Intelligence
Published in Kim Phuc Tran, Machine Learning and Probabilistic Graphical Models for Decision Support Systems, 2023
Thi Hien Nguyen, Huu Du Nguyen, Kim Duc Tran, Dinh Duy Kha Nguyen, Kim Phuc Tran
The unqualified products maybe because of the degradation of machines in the production lines after a certain time of working. The authors of35 pointed out that maintenance actions involve high costs, ranging from 15% to 70% of total production costs Therefore, it is necessary to have a good strategy for maintenance. Predictive maintenance refers to a well-known approach that allows determining the right time for maintenance activities. It benefits the production process by minimizing unplanned downtimes, reducing system faults, increasing efficiency in the use of resources, and planning maintenance interventions. Based on the data collected from many sensors appended to the machines, AI algorithms can predict, diagnose, and analyze future maintenance needs. In predictive maintenance, a key idea is to estimate the remaining useful life (RUL) - the remaining time before a machine requires a repair or a replacement when it no longer performs well its function. Several machine learning methods like Linear Regression, Bayesian Linear Regression, Poisson Regression, Neural Network Regression, Boosted Decision Tree Regression and Decision Forest Regression have been applied for RUL prediction in36 A hybrid model combining support vector machines for the prediction of the RUL of aircraft engines was presented in37. The deep learning algorithms like CNN, RNN, and Long-Short Term Memory (LSTM) network for estimating RUL can be seen in many studies like38;39;40;41
Analytics for Operations and Equipment Maintenance in Buildings and on Campuses
Published in John J. “Jack” Mc Gowan, Energy and Analytics, 2020
The goal of predictive maintenance is to save money and increase equipment reliability. Money can be saved by only making repairs or servicing equipment when necessary. The risk of equipment failure can be reduced by continuous, automated analysis of equipment performance in order to identify faults before they become critical. Whereas predictive maintenance was once limited to high-value capital assets, modern automation systems allow us to collect and store vast amounts of data, and low-cost computing power makes it possible to analyze that data.
Different Maintenance Types and The Need for Energy Centered Maintenance
Published in Marvin Howell, Fadi S. Alshakhshir, Energy Centered Maintenance—A Green Maintenance System, 2020
Marvin Howell, Fadi S. Alshakhshir
Increased cost in maintenance personnel. Predictive maintenance is different from preventive maintenance in that the actions are determined by the actual condition of the machine or equipment rather than on some preset schedule based on time between maintenance.
Solutions methods for m-machine blocking flow shop with setup times and preventive maintenance costs to minimise hierarchical objective-function
Published in International Journal of Production Research, 2023
Hugo Hissashi Miyata, Marcelo Seido Nagano, Jatinder N. D. Gupta
In this paper, maintenance is characterised by flexibility and diversity, concepts presented by Yu and Seif (2016). In sum, flexibility means that maintenance operations are not limited to be performed to fixed intervals. Predictive maintenance deals with decisions to maintain or not a system according to its state (Garg and Deshmukh 2006). CBM is a type of predictive maintenance and it involves monitoring the condition of a system, by quantifying some of its parameters (Azadeh, Asadzadeh, and Seif 2014). In this sense, flexible maintenance activities tries to simulate CBM without monitoring actions, estimating the remaining useful life of the system and the degradation that a job causes on that system during its processing. This degradation can be estimated by the quantity amount that a job deteriorates a certain machine. Diversity means that a machine has different types of maintenance activities, for example, one level describes the health of cleanliness of a filter while another level indicates the quality of engine's oil (Yu and Seif 2016). Additionally, duration of maintenance activities is considered prefixed.
Data-driven condition monitoring of two-stroke marine diesel engine piston rings with machine learning
Published in Ships and Offshore Structures, 2023
Ioannis Asimakopoulos, Luis David Avendaño-Valencia, Marie Lützen, Niels Gorm Maly Rytter
There are mainly three maintenance approaches that are followed with regards to machinery applications; reactive maintenance, preventive maintenance and condition-based or predictive maintenance (Gkerekos et al. 2016). Reactive maintenance occurs post-failure and depending on the criticality of the component and the state of the vessel, it can create large unnecessary costs. Preventive maintenance occurs on predetermined time intervals, and it provides a higher level of reliability. It also assumes that each component’s lifetime is split into a healthy period where the failure rate is very low and an unhealthy period where the failure rate gradually increases. A replacement of the component is usually mandatory within the early stages of the increasing failure rate period. Predictive maintenance uses sensor measurements and inspection data to monitor and diagnose the condition of components as well as estimate their remaining useful life.
Digital technologies for energy efficiency and decarbonization in mining
Published in CIM Journal, 2023
Although predictive maintenance applications focus on the ability to predict failures, which would lead to lower costs and better equipment utilization, these could also lead to reduced energy and carbon footprint if they are able to identify inefficient equipment use. Similarly, optimization models could be used for shrinking the mining carbon footprint if energy and GHG emissions were included as model parameters. Although 71% of emissions in the Canadian gold mining sub-sector were from diesel, electricity-related emissions can be significant for metal mines in general (Figure 1b). This may be due to different processes for other commodities or mines located in jurisdictions with more intense grid emissions. Nevertheless, in situations where these technologies are applied to electricity consuming processes like comminution or ventilation, digital technologies could benefit mines with significant electricity emissions.